Freshwater Biology (2001) 46, 1811±1831
Macrobenthic invertebrate richness and composition along a latitudinal gradient of European glacier-fed streams  KON ADALSTEINSSON,² JOHN E. BRITTAIN,³ EMMANUEL CASTELLA,* HA GISLI M. GISLASON,§ ANTHONY LEHMANN,* VALERIA LENCIONI,± BRIGITTE LODS-CROZET,* BRUNO MAIOLINI,± ALEXANDER M. MILNER,** J O N S . O L A F S S O N , § S V E I N J . S A L T V E I T ³ and D E B O R A H L . S N O O K * * *Laboratoire dÕEcologie et de Biologie Aquatique, Universite de GeneÁve, GeneÁve, Switzerland ²National Energy Authority, GrensaÂvegur, Reykjavõk, Iceland ³Freshwater Ecology and Inland Fisheries Laboratory (LFI), The Natural History Museums, University of Oslo, Blindern, Oslo, Norway §Institute of Biology, University of Iceland, GrensaÂvegur, Reykjavõk, Iceland ±Museo Tridentino di Scienze Naturali, Trento, Italy **School of Geography, The University of Birmingham, Edgbaston, Birmingham, U.K.
SUMMARY 1. The in¯uence of 11 environmental variables on benthic macroinvertebrate communities was examined in seven glacier-fed European streams ranging from Svalbard in the north to the Pyrenees in the south. Between 4 and 11 near-pristine reaches were studied on each stream in 1996±97. 2. Taxonomic richness, measured at the family or subfamily (for Chironomidae) levels for insects and higher levels for non-insects, increased with latitude from Svalbard (3 taxa) to the Pyrenees (29 taxa). 3. A Generalized Additive Model (GAM) incorporating channel stability [Pfankuch Index (PFAN)], tractive force, Froude number (FROU), water conductivity (COND), suspended solids (SUSP) concentration, and maximum temperature explained 79% of the total deviance of the taxonomic richness per reach. Water temperature and the PFAN of stability made the highest contribution to this deviance. In the model, richness response to temperature was positive linear, whereas the response to the PFAN was bell-shaped with an optimum at an intermediate level of stability. 4. Generalized Additive Models calculated for the 16 most frequent taxa explained between 25 (Tipulidae) and 79% (Heptageniidae) of the deviance. In 10 models, more than 50% of the deviance was explained and 11 models had cross-validation correlation ratios above 0.5. Maximum temperature, the PFAN, SUSP and tractive force (TRAC) were the most frequently incorporated explanatory variables. Season and substrate characteristics were very rarely incorporated. 5. Our results highlight the strong deterministic nature of zoobenthic communities in glacier-fed streams and the prominent role of water temperature and substrate stability in determining longitudinal patterns of macroinvertebrate community structure. The GAMs are proposed as a tool for predicting changes of zoobenthic communities in glacier-fed streams under climate or hydrological change scenarios.
Correspondence: Emmanuel Castella, Laboratoire dÕEcologie et de Biologie Aquatique, University of Geneva, 18 ch. des Clochettes, CH-1206 Geneva. E-mail:
[email protected] Ó 2001 Blackwell Science Ltd
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E. Castella et al. Keywords: Alpine, Arctic, climate change, generalized additive models, glacial stream, macrobenthos
Introduction Current climate change scenarios indicate proportionally more detectable impacts at both high altitude and latitudes (Roots, 1989; Beniston, Diaz & Bradley, 1997; Beniston, 2000). The precise distribution of climate change cannot be established, especially for complex mountain regions, but in areas where Ôcoupled transitions in vegetation and precipitation patterns occur, geomorphological systems may be near thresholds of change and ecological systems may be more vulnerableÕ (Poff, 1992). Among the potentially sensitive ecological systems, Grimm (1993) proposed streams as models to examine the consequences of climate changes. Indeed, running water systems and their biota can be regarded as catchment-scale integrative monitors for a set of hydrological, thermal and biotic variables that might be modi®ed by climate change. It follows that arctic and alpine running waters can be regarded as research foci in such a context, and their communities considered to be as much under threat as terrestrial alpine communities (New, 1995). Kryal streams and rivers dominated by glacial ¯ow provide comparatively the highest amount of published information compared with other types of alpine and arctic running waters (Ward, 1994). Kryal zoobenthic communities appear to be largely controlled by thermal and substrate stability variables. Following the conceptual model by Milner & Petts (1994), it seemed possible to model the longitudinal distribution of macrofauna in glacial streams on the basis of environmental variables. However, unlike lowland streams in which climate change is thought to lead to an increase in water temperature (Stefan & Sinokrot, 1993; Hogg et al., 1995; Sinokrot et al., 1995), the anticipated glacier ablation would result in a decrease in stream water temperature and therefore a downstream expansion of the kryal in¯uence and associated fauna (McGregor et al., 1995). There might be considerable variation however, depending on glacier size and geographical location, and kryal streams fed by small glacial areas might even undergo a reduction in their kryal fauna. The recent availability of powerful statistical techniques has led to the current expansion of predictive habitat distribution models (Guisan & Zimmermann, 2000). Generalized Additive Models (GAMs) are a
non-parametric extension of multiple regressions and Generalized Linear Models (GLM) (Hastie & Tibshirani, 1990). Generalizd Additive Models are ¯exible exploratory and modelling tools as they allow for linear and non-linear response shapes, for both continuous and factor variables, and for a combination of those within a single model. They are less restrictive than classical linear regressions or GLMs because they are more data than model driven. Yee & Mitchell (1991) provided a comprehensive introduction to GAM modelling in ecology. Since then, GAMs became more frequently used to model species response to environmental variables, especially in vegetation sciences (e.g. Leathwick, 1995; Heegard, 1997; Bio, Alkemade & Barendregt, 1998; Lehmann, 1998; Austin, 1999; but see Brosse & Lek, 2000 for ®sh microhabitat modelling or Fewster et al., 2000 for birds). Yee & Mitchell (1991) concluded that GAMs are appropriate for modelling potential changes in species distributions resulting from global warming. The goal of this study was to develop predictive models for macroinvertebrate taxonomic richness, and the abundance of major macroinvertebrate taxa along longitudinal/altitudinal gradients in glacial rivers. Development of these models also represents an attempt to test the validity of and add a quantitative component to the conceptual model proposed by Milner & Petts (1994). Generalized Additive Models were applied in this study because their data-driven smoothing regression technique has been shown to provide improvements over classical regression models, especially because of the avoidance of the a priori assumption of ®xed response shapes (Bio et al., 1998). The data used for model development originated from seven glacial streams covering a wide European latitudinal gradient from Svalbard to the Pyrenees (Brittain & Milner, 2001). They were studied under a common protocol within the ÔArctic and Alpine Stream Ecosystem ResearchÕ project (AASER) (Brittain et al., 2000; Brittain & Milner, 2001).
Methods The individual studies carried out under the AASER project are detailed in Brittain et al. (2001), Gislason et al. (2001), Lods-Crozet et al. (2001), Maiolini & Lencioni (2001), and Snook & Milner (2001). Ó 2001 Blackwell Science Ltd, Freshwater Biology, 46, 1811±1831
Ó 2001 Blackwell Science Ltd, Freshwater Biology, 46, 1811±1831
No trees 0.01±15.6 5±50 300±2900 4 483
TAI CON MUT BRI LEI
WJO
BAY
Taillon Conca Mutt Dalelva LeirungsaÊi
W-JokuÈlsaÂ
Bayelva
*c. 300 mm recorded during July and August. ²Part of a large plateau glacier with an area of 487 km2. ³The natural treeline, without anthropogenic impacts, would be 300±400 m a.s.l.
12 30.9 742 78°55¢N, 11°50¢E
No trees³ 25±31 160±860 5±45 000 11 662 68 840 1800 64°50¢N, 18°45¢W
0.3±0.4 0.3±2.1 0.6±1.5 0.5±35 0.4±6.2 50±1500 1870±2500 350±4600 1300±2830 5±3600 1800±2600 100±7100 10±340 200±24 600 970±1550 4 7 5 5 7 1497 1595 1270 c. 800
*
0.2 0.2 0.6 22² 1.2 6.4 4.2 7 25.6 400 2975 3463 3099 1915 2159 0°01¢W 10°36¢E 8°24¢E 6°50¢E 8°41¢E 43°06¢N, 46°06¢N, 46°33¢N, 61°40¢N, 61°24¢N,
Co-ordinates Region
Pyrenees S.E. Alps N. Alps W. Norway Central Norway Central Iceland Spitsbergen, Svalbard
Discharge range (m3s±1) Altitudinal range of river studied (m a.s.l) Distance of reaches from glacier (m) Number of study reaches Precipiation (mm) 1996/1997 Catchment area at Glacier downstream area reach (km2) (km2) Max. altitude of catchment (m a.s.l.) Code
Geomorphological description (width of valley ¯oor, of all active channels, slope) was carried out at the onset of the project. The stream bottom component of the PfankuchÕs index (PFAN) (Pfankuch, 1975) was used to assess channel stability by scoring ®ve variables (rock angularity, bed-surface brightness,
River system
Geomorphological and environmental variables
Table 1 Location and characteristics of the glacier-fed rivers investigated in the AASER project
The reaches were located in seven glacier-melt dominated streams (Brittain & Milner, 2001). The glacial streams formed a latitudinal and altitudinal gradient across Europe from the Pyrenees in the south to Svalbard in the north (Table 1). A common protocol was used for determining the major geomorphological, physical, chemical and biological components of these streams. Four to eight 15 m long reaches were de®ned in each stream to represent the different sectors identi®ed on the basis of valley and channel geomorphology. In each stream, the ®rst reach was as close to the glacial snout as possible. The second reach was typically within 1000 m of the glacier snout and upstream of any major tributary input. The downstream limit of the study sector was where a fully developed invertebrate community occurred, i.e. where Chironomidae were at least associated with Ephemeroptera (Baetidae and other families), Plecoptera (Nemouridae, Chloroperlidae and other families), and Trichoptera. This downstream limit was derived from preliminary surveys of the streams. It was applied within zoogeographical constraints to account for the fact that certain taxa are absent from northern and arctic catchments. At each reach, except Bayelva on Svalbard, ®eld surveys were carried out at three time periods during both 1996 and 1997: immediately post spring snowmelt (June), in mid-summer during the ice melt (August) and at low water level (September). These time periods will be refered to as `seasons'. The results obtained at a given reach and a given sampling season served as units in the analyses. These units will be referred to as 'reach-date'. The snowpack precluded some samplings in upstream reaches in June. As the ice free season on Svalbard is short and because of logistic constraints, Bayelva was sampled only during early July and late August 1997.
Treeline altitude (m a.s.l.)
Reaches and sampling regime
1600 2000 2000 600 1050
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particle packing, percentage stable materials, scouring, presence and type of aquatic vegetation). Scores were summed to provide an overall index of channel stability with a potential range of 15±70 (high scores representing unstable channels at the reach scale). During a 5-day sampling period at each ®eld survey, water temperature, level, discharge, conductivity and suspended solids (SUSP) were monitored at minimum and maximum ¯ows on the upstream and downstream reach. At each of four transects installed at all reaches, the wetted channel width to a maximum depth of 0.5 m and depth/velocity pro®les were determined. At the reach scale, average current velocity and average depth were combined to calculate an average Froude number (FROU, dimensionless), according to Statzner et al. (1988): FROU U=
g D0:5 with U: mean current velocity, g: gravity acceleration, D: mean water depth (from the depth pro®les). The reach slope and water depth were combined to calculate tractive force (TRAC, dyn cm±1) (shear stress) according to Statzner et al. (1988): TRAC g S D q with g: gravity acceleration, S: reach slope, D: mean water depth, q: water density. Digital temperature loggers placed in streams throughout the study period monitored water temperature continuously at most reaches. Visual or manual assessments at each point of the depth/velocity pro®les were used to record the bed-sediment com-
position, which was expressed for the reach as the percentage cover of four categories (boulders >20 cm, coarse gravel 5±20 cm, ®ne gravels 0.2±5 cm, and ®ne particles
